However, calculating the key rate by numerical methods requires minimizing a convex function over all eavesdropping attacks related with the experimental data [1]}, [2]}. The efficiency of this optimization depends on the number of parameters of the QKD protocol. For example, in discrete modulation CV-QKD, the number of parameters is generally \(1000-3000\)  depending on the different choices of photon-number cutoff [3]}. This leads to the corresponding optimization may take minutes or even hours [4]}. On the other hand, with the development of global quantum networks [5]}
, there is an increasing interest in implementing the QKD protocols on moving platforms [6]}, [7]}, [8]}, which requires low latency and low cost for calculating key rates. Therefore, it is especially important to develop tools for calculating the key rate that are more efficient than numerical methods.
